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VVBPConvNet: a lightweight convolutional network for sparse-view CT reconstruction
Optics Express
|August 14, 2026
Summary
Sparse-view computed tomography (CT) reduces radiation dose but causes artifacts. A new View-by-View Backprojection Convolutional Network (VVBPConvNet) improves image quality by preserving projection data, outperforming existing methods.
Area of Science:
- Medical Imaging
- Computer Vision
- Radiology
Background:
- Sparse-view computed tomography (CT) reduces radiation exposure but suffers from artifacts and loss of detail due to limited angular sampling.
- Current reconstruction methods often degrade projection information during image formation, limiting sparse-view CT performance.
Purpose of the Study:
- To develop an advanced deep learning method for high-quality sparse-view CT reconstruction.
- To preserve and leverage geometric information from individual projection views more effectively.
Main Methods:
- Proposed a novel View-by-View Backprojection Convolutional Network (VVBPConvNet) operating on view-by-view backprojection tensors.
- Developed a lightweight network architecture to balance reconstruction performance and computational efficiency.
- Evaluated the method on simulated and clinical datasets.
Main Results:
- VVBPConvNet demonstrated superior performance compared to state-of-the-art analytical, iterative, and deep learning methods.
- Achieved significant artifact suppression and preservation of fine anatomical structures.
- Showed robust performance in sparse-view and noisy imaging conditions.
Conclusions:
- Leveraging view-by-view backprojection representations is an effective strategy for information-preserving sparse-view CT reconstruction.
- VVBPConvNet offers a practical solution for improving sparse-view CT image quality and reducing radiation dose.
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